langchain-skills

Using the Deep Agents CLI - terminal interface, persistent memory with AGENTS.md, project conventions, skills directories, and CLI commands.

Understanding Deep Agents framework - what they are, how to create them with createDeepAgent, and the agent harness architecture with built-in middleware for planning, filesystems, and subagents.

Creating and using custom skills with progressive disclosure, SKILL.md format, and the Agent Skills protocol in Deep Agents.

Using the Deep Agents CLI - terminal interface, persistent memory with AGENTS.md, project conventions, skills directories, and CLI commands.

Using FilesystemMiddleware with virtual filesystems, backends (State, Store, Filesystem, Composite), and context management for Deep Agents.

Using FilesystemMiddleware with virtual filesystems, backends (State, Store, Filesystem, Composite), and context management for Deep Agents.

Implementing human-in-the-loop approval workflows with interruptOn parameter for sensitive tool operations in Deep Agents.

Implementing human-in-the-loop approval workflows with interrupt_on parameter for sensitive tool operations in Deep Agents.

Implementing long-term memory in Deep Agents with cross-session storage using StoreBackend, CompositeBackend, and InMemoryStore for persistent data.

Implementing long-term memory in Deep Agents with cross-session storage using StoreBackend, CompositeBackend, and InMemoryStore for persistent data.

Understanding Deep Agents framework - what they are, how to create them with create_deep_agent/createDeepAgent, and the agent harness architecture with built-in middleware for planning, filesystems, and subagents.

Creating and using custom skills with progressive disclosure, SKILL.md format, and the Agent Skills protocol in Deep Agents.

Using SubAgentMiddleware to spawn subagents for task delegation, context isolation, and specialized work in Deep Agents.

Using SubAgentMiddleware to spawn subagents for task delegation, context isolation, and specialized work in Deep Agents.

Using TodoListMiddleware for task planning and tracking progress with the write_todos tool in Deep Agents for complex multi-step workflows.

Using TodoListMiddleware for task planning and tracking progress with the write_todos tool in Deep Agents for complex multi-step workflows.

Create and use LangChain agents with createAgent - includes agent loops, ReAct pattern, tool execution, and state management

Create and use LangChain agents with create_agent - includes agent loops, ReAct pattern, tool execution, and state management

Add human oversight to LangChain agents using HITL middleware - includes interrupts, approval workflows, edit/reject decisions, and checkpoints

Add human oversight to LangChain agents using HITL middleware - includes interrupts, approval workflows, edit/reject decisions, and checkpoints

Guide to using chat model integrations in LangChain including OpenAI, Anthropic, Google, Azure, and Bedrock

Guide to using chat model integrations in LangChain including OpenAI, Anthropic, Google, Azure, and Bedrock

Guide to using document loader integrations in LangChain for PDFs, web pages, text files, and APIs

Guide to using document loader integrations in LangChain for PDFs, web pages, text files, and APIs

Guide to using embedding model integrations in LangChain including OpenAI, Azure, and local embeddings

Guide to using embedding model integrations in LangChain including OpenAI, Azure, and local embeddings

Guide to using text splitter integrations in LangChain including recursive, character, and semantic splitters

Guide to using text splitter integrations in LangChain including recursive, character, and semantic splitters

Guide to using tool integrations in LangChain including pre-built toolkits, Tavily, Wikipedia, and custom tools

Guide to using tool integrations in LangChain including pre-built toolkits, Tavily, Wikipedia, and custom tools

Guide to using vector store integrations in LangChain including Chroma, Pinecone, FAISS, and memory vector stores

Guide to using vector store integrations in LangChain including Chroma, Pinecone, FAISS, and memory vector stores

Initialize and use LangChain chat models - includes provider selection (OpenAI, Anthropic, Google), model configuration, and invocation patterns

Initialize and use LangChain chat models - includes provider selection (OpenAI, Anthropic, Google), model configuration, and invocation patterns

Work with multimodal inputs/outputs in LangChain - includes images, audio, video, content blocks, and vision capabilities

Work with multimodal inputs/outputs in LangChain - includes images, audio, video, content blocks, and vision capabilities

Build Retrieval Augmented Generation (RAG) systems with LangChain - includes embeddings, vector stores, retrievers, document loaders, and text splitting

Build Retrieval Augmented Generation (RAG) systems with LangChain - includes embeddings, vector stores, retrievers, document loaders, and text splitting

Stream outputs from LangChain agents and models - includes stream modes, token streaming, progress updates, and real-time feedback

Stream outputs from LangChain agents and models - includes stream modes, token streaming, progress updates, and real-time feedback